LEADER 06605nam 2200793 a 450 001 9910808250103321 005 20230912153140.0 010 $a9786613656148 010 $a9781280679216 010 $a1280679212 010 $a9781118401330 010 $a1118401336 010 $a9781118401323 010 $a1118401328 010 $a9781118401309 010 $a1118401301 035 $a(CKB)2560000000082834 035 $a(EBL)894426 035 $a(SSID)ssj0000678683 035 $a(PQKBManifestationID)11417447 035 $a(PQKBTitleCode)TC0000678683 035 $a(PQKBWorkID)10728907 035 $a(PQKB)11178035 035 $a(Au-PeEL)EBL894426 035 $a(CaPaEBR)ebr10630604 035 $a(Au-PeEL)EBL4034854 035 $a(CaPaEBR)ebr11110077 035 $a(CaONFJC)MIL365614 035 $a(OCoLC)795998928 035 $a(CaSebORM)9781118401330 035 $a(MiAaPQ)EBC894426 035 $a(MiAaPQ)EBC4034854 035 $a(OCoLC)828688523 035 $a(OCoLC)ocn828688523 035 $a(Perlego)1014806 035 $a(EXLCZ)992560000000082834 100 $a20111223d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe physics of microdroplets /$fJean Berthier and Kenneth A. Brakke 205 $a1st edition 210 $aHoboken, N.J. $cJohn Wiley & Sons, Inc ;$aSalem, Mass. $cScrivener Pub. LLC$d2012 215 $a1 online resource (392 p.) 300 $aDescription based upon print version of record. 311 08$a9780470938805 311 08$a0470938803 320 $aIncludes bibliographical references and index. 327 $aMachine generated contents note: Preface xviii Acknowledgements xxi Introduction 1 1. Fundamentals of Capillarity 5 1.1 Abstract 5 -- 1.2 Interfaces and Surface Tension 5 -- 1.3 Laplace's Law and Applications 13 -- 1.4 Measuring the Surface Tension of Liquids 48 -- 1.5 Minimization of the Surface Energy and Minimal Surfaces 59 -- 1.6 References 61 -- 2. Minimal Energy and Stability Rubrics 65 -- 2.1 Abstract 65 -- 2.2 Spherical Shapes as Energy Minimizers 66 -- 2.3 Symmetrization and the Rouloids 70 -- 2.4 Increasing Pressure and Stability 75 -- 2.5 The Double-Bubble Instability 78 -- 2.6 Conclusion 81 -- 2.7 References 82 -- 3. Droplets: Shape, Surface and Volume 83 -- 3.1 Abstract 83 -- 3.2 The Shape of Micro-drops 84 -- 3.3 Electric Bonds Number 85 -- 3.4 Shape, Surface Area and Volume of Sessile Droplets 85 -- 3.5 Conclusion 103 -- 3.6 References 103 -- 4. Sessile Droplets 105 -- 4.1 Abstract 105 -- 4.2 Droplet Self-motion Under the Effect of a Contrast or Gradient of Wettability 105 -- 4.3 Contact Angle Hysteresis 112 -- 4.4 Pinning and Canthotaxis 115 -- 4.5 Sessile Droplet on a Non-ideally Planar Surface 122 -- 4.6 Droplet on Textured or Patterned Substrates 123 -- 4.7 References 140 -- 5. Droplets Between Two Non-parallel Planes: from Tapered Planes to Wedges 143 -- 5.1 Abstract 143 -- 5.2 Droplet Self-motion Between Two Non-parallel Planes 143 -- 5.3 Droplet in a Corner 151 -- 5.4 Conclusion 159 -- 5.5 References 159 -- 6. Microdrops in Microchannels and Microchambers 161 -- 6.1 Abstract 161 -- 6.2 Droplets in Micro-wells 161 -- 6.3 Droplets in Microchannels 167 -- 6.4 Conclusion 180 -- 6.5 References 180 -- 7. Capillary Effects: Capillary Rise, Capillary Pumping, and Capillary Valve 183 -- 7.1 Abstract 183 -- 7.2 Capillary Rise 183 -- 7.3 Capillary Pumping 195 -- 7.4 Capillary Valves 202 -- 7.5 Conclusions 207 -- 7.6 References 207 -- 8. Open Microfluidics 209 -- 8.1 Abstract 209 -- 8.2 Droplet Pierced by a Wire 210 -- 8.3 Liquid Spreading Between Solid Structures - Spontaneous Capillary Flow 216 -- 8.4 Liquid Wetting Fibers 239 -- 8.5 Conclusions 247 -- 8.6 References 248 -- 8.7 Appendix: Calculation of the Laplace Pressure for a Droplet on a Horizontal Cylindrical Wire 250 -- 9. Droplets, particles and Interfaces 251 -- 9.1 Abstract 251 -- 9.2 Neumann's Construction for liquid Droplets 251 -- 9.3 The Difference Between Liquid Droplets and Rigid Spheres at an Interface 252 -- 9.4 Liquid Droplet Deposited at a Liquid Surface 253 -- 9.5 Immiscible Droplets in Contact and Engulfment 258 -- 9.6 Non-deformable (Rigid) Sphere at an Interface 262 -- 9.7 Droplet Evaporation and Capillary Assembly 275 -- 9.8 Conclusion 288 -- 9.9 References 290 -- 10. Digital Microfluidics 293 -- 10.1 Abstract 293 -- 10.2 Electrowetting and EWOD 293 -- 10.3 Droplet Manipulation with EWOD 304 -- 10.4 Examples of EWOD in Biotechnology - Cell Manipulation 333 -- 10.5 Examples of Electrowetting for Optics-Tunable Lenses and Electrofluidic Display 335 -- 10.6 Conclusion 336 -- 10.7 References 337 -- 11. Capillary Self-assembly for 3D Microelectronics 341 -- 11.1 Abstract 341 -- 11.2 Ideal Case: Total Pinning on the Chip and Pad Edges 342 -- 11.3 Real Case: Spreading and Wetting 352 -- 11.4 The Importance of Pinning and Confinement 355 -- 11.5 Conclusion 357 -- 11.6 Appendix A: Shift Energy and Restoring Force 358 -- 11.7 Appendix B: Twist Energy and Restoring Torque 359 -- 11.8 Appendix C: Lift Energy and Restoring Force 361 -- 11.9 References 362 -- 12. Epilogue 365 -- Index 367. 330 $a"This book aims to give the reader the theoretical and numerical tools to understand, explain, calculate and predict the often non intuitive, observed behaviour of droplets in microsystems. After a chapter dedicated to the general theory of wetting, the book successively. Presents the theory of 3D liquid interfaces, gives the formulas for volume and surface of sessile and pancake droplets, analyses the behaviour of sessile droplets, analyses the behaviour of droplets between tapered plates and in wedges, presents the behaviour of droplets in microchannels investigates the effect of capillarity with the analysis of capillary rise, treats the onset of spontaneous capillary flow in open microfluidic systems, analyses the interaction between droplets, like engulfment, presents the theory and application of electrowetting"--$cProvided by publisher. 606 $aMicrodroplets 606 $aMicrofluidics 606 $aCapillarity 615 0$aMicrodroplets. 615 0$aMicrofluidics. 615 0$aCapillarity. 676 $a532/.05 686 $aTEC008070$2bisacsh 700 $aBerthier$b Jean$f1952-$01622764 701 $aBrakke$b Kenneth A$045243 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910808250103321 996 $aThe physics of microdroplets$94039891 997 $aUNINA LEADER 04027nam 22005775 450 001 9910678246803321 005 20251008163538.0 010 $a9789811989346 010 $a9811989346 024 7 $a10.1007/978-981-19-8934-6 035 $a(MiAaPQ)EBC7211139 035 $a(Au-PeEL)EBL7211139 035 $a(CKB)26240746500041 035 $a(DE-He213)978-981-19-8934-6 035 $a(PPN)269094741 035 $a(EXLCZ)9926240746500041 100 $a20230307d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDynamic Network Representation Based on Latent Factorization of Tensors /$fby Hao Wu, Xuke Wu, Xin Luo 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (89 pages) 225 1 $aSpringerBriefs in Computer Science,$x2191-5776 311 08$aPrint version: Wu, Hao Dynamic Network Representation Based on Latent Factorization of Tensors Singapore : Springer,c2023 9789811989339 320 $aIncludes bibliographical references and index. 327 $aChapter 1 IntroductionChapter -- 2 Multiple Biases-Incorporated Latent Factorization of tensors -- Chapter 3 PID-Incorporated Latent Factorization of Tensors -- Chapter 4 Diverse Biases Nonnegative Latent Factorization of Tensors -- Chapter 5 ADMM-Based Nonnegative Latent Factorization of Tensors -- Chapter 6 Perspectives and Conclusion. . 330 $aA dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes? various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge. In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency. 410 0$aSpringerBriefs in Computer Science,$x2191-5776 606 $aArtificial intelligence$xData processing 606 $aQuantitative research 606 $aData Science 606 $aData Analysis and Big Data 615 0$aArtificial intelligence$xData processing. 615 0$aQuantitative research. 615 14$aData Science. 615 24$aData Analysis and Big Data. 676 $a515.63 700 $aWu$b Hao$01062638 702 $aWu$b Xuke 702 $aLuo$b Xin 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910678246803321 996 $aDynamic Network Representation Based on Latent Factorization of Tensors$93071628 997 $aUNINA